Dynamic Models of Appraisal Networks Explaining Collective Learning

TL;DR

This paper introduces dynamic models of collective learning in teams, integrating appraisal networks, influence, and skill levels, grounded in evolutionary game theory and sociology, to explain how rational behavior and suboptimality emerge.

Contribution

It develops a series of interconnected models capturing team learning processes, incorporating social influence and appraisal networks, advancing understanding of collective decision-making.

Findings

Models demonstrate how rational optimal behavior emerges.

Conditions leading to suboptimal learning are identified.

Framework integrates evolutionary dynamics with social influence.

Abstract

This paper proposes models of learning process in teams of individuals who collectively execute a sequence of tasks and whose actions are determined by individual skill levels and networks of interpersonal appraisals and influence. The closely-related proposed models have increasing complexity, starting with a centralized manager-based assignment and learning model, and finishing with a social model of interpersonal appraisal, assignments, learning, and influences. We show how rational optimal behavior arises along the task sequence for each model, and discuss conditions of suboptimality. Our models are grounded in replicator dynamics from evolutionary games, influence networks from mathematical sociology, and transactive memory systems from organization science.